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Background: Currently, Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed the field of agriculture with the innovative idea of automation and intelligence. The agriculture field completely relies on the uncertainty parameter of soil, atmosphere, and water. Technological advancement in IoT and AI assist in resolving this uncertainty factor and recommend the best crops to the farmers so that they can also enhance the productivity of the crops and meet the world's large food demand smartly. Objective: In this paper, we have suggested an IoT and AI-based model which trained with 2200 records of the dataset and seven attributes in Python. The model suggests 22 different crops to farmers after collecting samples through different sensor data. We used soil, temperature, humidity, pH, and rainfall sensors. Soil sensors were used to measure the amount of N, P, and K in soil. Method: The samples of of the SLE patients, Cell culture and treatment, Plasmid construction and transfection, Quantitative real-time PCR analysis, Enzyme-linked immunosorbent assay (ELISA), Cell viability analysis, Cell apoptosis analysis, Western blot were collected. Result: In this research, we investigated the contribution of GAS5 in the pathogenesis of SLE. We confirmed that, compared to healthy people, the expression of GAS5 was significantly decreased in peripheral monocytes of SLE patients. Subsequently, we found that GAS5 can inhibit the proliferation and promote the apoptosis of monocytes by over-expressing or knocking down the expression of GAS5. Additionally, the expression of GAS5 was suppressed by LPS. Silencing GAS5 significantly increased the expression of a group of chemokines and cytokines, including IL-1β, IL-6 and THFα, which were induced by LPS. Furthermore, it was identified that the involvement of GAS5 in TLR4-mediated inflammatory process was through affecting the activation of the MAPK signaling pathway. Conclusion: In general, the decreased GAS5 expression may be a potential contributor to the elevated production of a great number of cytokines and chemokines in SLE patients. And our research suggests that GAS5 contributes a regulatory role in the pathogenesis of SLE, and may provide a potential target for therapeutic intervention.
Background: Currently, Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed the field of agriculture with the innovative idea of automation and intelligence. The agriculture field completely relies on the uncertainty parameter of soil, atmosphere, and water. Technological advancement in IoT and AI assist in resolving this uncertainty factor and recommend the best crops to the farmers so that they can also enhance the productivity of the crops and meet the world's large food demand smartly. Objective: In this paper, we have suggested an IoT and AI-based model which trained with 2200 records of the dataset and seven attributes in Python. The model suggests 22 different crops to farmers after collecting samples through different sensor data. We used soil, temperature, humidity, pH, and rainfall sensors. Soil sensors were used to measure the amount of N, P, and K in soil. Method: The samples of of the SLE patients, Cell culture and treatment, Plasmid construction and transfection, Quantitative real-time PCR analysis, Enzyme-linked immunosorbent assay (ELISA), Cell viability analysis, Cell apoptosis analysis, Western blot were collected. Result: In this research, we investigated the contribution of GAS5 in the pathogenesis of SLE. We confirmed that, compared to healthy people, the expression of GAS5 was significantly decreased in peripheral monocytes of SLE patients. Subsequently, we found that GAS5 can inhibit the proliferation and promote the apoptosis of monocytes by over-expressing or knocking down the expression of GAS5. Additionally, the expression of GAS5 was suppressed by LPS. Silencing GAS5 significantly increased the expression of a group of chemokines and cytokines, including IL-1β, IL-6 and THFα, which were induced by LPS. Furthermore, it was identified that the involvement of GAS5 in TLR4-mediated inflammatory process was through affecting the activation of the MAPK signaling pathway. Conclusion: In general, the decreased GAS5 expression may be a potential contributor to the elevated production of a great number of cytokines and chemokines in SLE patients. And our research suggests that GAS5 contributes a regulatory role in the pathogenesis of SLE, and may provide a potential target for therapeutic intervention.
After the advent of 5th generation (5G) and 6th generation (6G) cellular networks, the complexity of managing real-time signal interference has increased in dense and dynamic environments. Traditional interference techniques, such as frequency reuse and allocation, while effective, lack robust adaptability and transparency needed to reduce interference in advanced communication networks. This paper introduces a novel approach that fuses large language models (LLMs) and Explainable Artificial Intelligence (XAI) to mitigate interference and enhance interference management in the mathematical foundations of 6G networks. The proposed approach provides accurate interference predictions, which the LLM balances with its complex architecture, necessary to meet the demands of beyond 5G and 6G networks, along with interpretable explanations to ensure transparency in decision-making. The proposed framework has been evaluated across various performance metrics. Interference latency consistently achieves lower rates of 0.95 s, compared to traditional techniques, which average around 1 s. Furthermore, the confidence score of the LLM shows a stable value of 0.87 throughout the system, compared to 0.85 in techniques without LLMs. Overall, the XAI-driven LLM demonstrates the potential of incorporating LLMs and XAI into wireless networks to improve resilience in next-generation networks. This proof of concept introduces a novel framework that offers new dimensions in wireless communication, particularly for interference management, prediction, and mitigation.
Driver drowsiness is one of the major problems that every country is facing. The ICT sector is continuously investing in the automaker industry worldwide to bring about digital transformation in existing vehicles and driving. The smart behavior of vehicles is becoming possible with the convergence of intelligent manufacturing, AI, and IoT. In this chapter, the authors are presenting a framework for efficient detection of driver's drowsiness by utilizing the power of deep learning technology. The use of convolution neural network (CNN) is explored, and the system is developed and tested using different activation functions. The proposed driver drowsiness framework is able to signify the drowsiness state of the driver and to automatically alert the driver. The accuracy of the proposed model is compared at different activation functions such as ReLu, SeLu, Sigmoidal, Tanh, and SoftPlus, and higher accuracy is achieved with ReLu as 98.21%.
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